• Energy Research

Abstract Polytetrafluoroethylene (PTFE) is widely employed to improve the hydrophobicity of gas diffusion layer (GDL) in proton exchange membrane (PEM) fuel cells. In this study, the effects of different PTFE loadings on the relationship of the capillary pressure Pc and water saturation s in the mixed-wettability GDL, i.e. Pc-s, are investigated using a three-dimensional (3D) volume of fluid (VOF) model. The simulated Pc-s curves are presented and compared with results obtained from the lattice Boltzmann model (LBM) and experiments. The good agreement between the VOF predictions and experiment data is achieved, indicating that the mixed wettability in the PTFE treated GDL is an important feature to understand two-phase behaviors in fuel cells. The homogeneous and heterogeneous PTFE distributions resulted from two PTFE drying methods (i.e. the vacuum and air dryings, respectively) are studied. It was found that the air drying GDL yields a high PTFE concentration near the water inlet and reduces water imbibition near the inlet. The simulated Pc-s correlation from VOF model was compared with standard Leverett correlation.

In this paper, a new formulation and an efficient numerical technique are preliminarily studied for a nonisothermal, anisotropic, two-phase transport model of PEMFC, where flow, species, charge and energy equations are all addressed. The importance of water and temperature management are investigated in the anisotropic and nonisothermal point of view. Due to the employment of multi-phase mixture (M2) model, the diffusivity of water transport presents the significant discontinuity and degeneracy across the interface of single gas phase region and two-phase region. In addition, the distinct discontinuity of water diffusivity also emerges through the membrane. Such discontinuities and degeneracy of water diffusivity challenge the fast convergence of nonlinear iteration in numerical simulation, showing oscillating and even divergent iteration process. Based on an intensive new formulation of M2 model for PEMFC, an efficient numerical technique, Kirchhoff transformation, is specifically employed in order to overcome such numerical difficulties and achieve fast and convergent simulation. Numerical experiment is implemented accordingly to indicate the efficiency of the presented numerical technique, in contrast to the oscillating iterations without new numerical technique.

This paper investigates the heat and water transport in a fuel cell during startup in subzero environment. Followed by the work of Wang [1], we further evaluate the key parameters that govern the startup of fuel cells with no external heating by considering the range of the relevant factors for a typical fuel cell. Specifically, these key parameters include characteristic times scales of cell warm up , membrane hydration in the catalyst layer , ice buildup , and ice melting , and the ratio of the two important time constants . The results give the ranges of these parameters and therefore are important for material and property selection in fuel cell design for better cold-start characteristics.

This work presents an in situ study on the water-content measurement in polymer electrolyte fuel cells (PEFCs) using neutron imaging. The effects of several important operating and design parameters on water content are examined, including the relative humidity (RH), the polytetraflouroethylene (PTFE) loadings in gas diffusion media including the microporous layer (MPL), current density, and flow field configurations including single-/quad-serpentine channels and co-/counter-flow configurations. Efforts are also made to distinguish water contents between the channel and land projected areas, and obtain the water profile along the gas flow path. We find that the highest water content occurs at a low current density for fixed operational stoichiometry, and liquid water emerges downstream at low humidity and increases rapidly after on-set.

Author(s): Mishler, J; Wang, Y; Lujan, R; Mukundan, R; Borup, RL | Abstract: The ability of polymer electrolyte fuel cells (PEFCs) to startup at subfreezing temperatures is governed by whether it is able to overcome the freezing point (0°C) before product ice prevents the electrochemical reactions. In this work, we experimentally investigated the coulombs of charge Qc transferred in PEFCs under subfreezing operation before the output voltage drops to 0.0V. PEFCs with various membranes and catalyst-layer thicknesses, ionomer-carbon ratios, operating current density, and initial hydration of PEFCs were studied, and their influences on cold-start performance and coulombs of charge were experimentally measured. We find that subfreezing temperature, ionomer-catalyst ratio, and catalyst-layer thickness, significantly affect the amount of charge transferred before operational failure, whereas the membrane thickness and initial hydration level have limited effect for the considered cases. © 2013 The Electrochemical Society.

This paper investigates the electrochemical kinetics, oxygen transport, and solid water formation in polymer electrolyte fuel cell (PEFC) during cold start. Following [Y. Wang, J. Electrochem. Soc., 154 (2007) B1041-B1048.], we develop a pseudo one-dimensional analysis, which enables the evaluation of the impact of ice volume fraction and temperature variations on cell performance during cold-start. The oxygen profile, starvation ice volume fraction, and relevant overpotentials are obtained. This study is valuable for studying the characteristics of PEFC cold-start.

Abstract PEM (Polymer Electrolyte Membrane) fuel cells have the potential to reduce our energy use, pollutant emissions, and dependence on fossil fuels. In the past decade, significant advances have been achieved for commercializing the technology. For example, several PEM fuel cell buses are currently rated at the technical readiness stage of full-scale validation in realistic driving environments and have met or closely met the ultimate 25,000-h target set by the U.S. Department of Energy. So far, Toyota has sold more than 4000 Mirai PEM fuel cell vehicles (FCVs). Over 30 hydrogen gas stations are being operated throughout the U.S. and over 60 in Germany. In this review, we cover the material, design, fundamental, and manufacturing aspects of PEM fuel cells with a focus on the portable, automobile, airplane, and space applications that require careful consideration in system design and materials. The technological status and challenges faced by PEM fuel cells toward their commercialization in these applications are described and explained. Fundamental issues that are key to fuel cell design, operational control, and material development, such as water and thermal management, dynamic operation, cold start, channel two-phase flow, and low-humidity operation, are discussed. Fuels and fuel tanks pertinent to PEM fuel cells are briefly evaluated. The objective of this review is three fold: (1) to present the latest status of PEM fuel cell technology development and applications in the portable and transportation power through an overview of the state of the art and most recent technological advances; (2) to describe materials and water/thermal transport management for fuel cell design and operational control; and (3) to outline major challenges in the technology development and the needs for fundamental research for the near future and prior to fuel cell world-wide deployment.

Abstract The maximum achievable power of a polymer electrolyte membrane (PEM) fuel cell under specific operating temperature is important to its application. In this paper, we propose a method that integrates an artificial neural network (ANN) with the genetic algorithm (GA) to predict the performance of a PEM fuel cell and identify its maximum powers and corresponding conditions for operational control purpose. A validated three-dimensional (3D) multiphysics model is employed to generate total 1500 data points for training, testing, and verifying the ANN, which consists of two hidden layers with eight and four neurons on each hidden layer, respectively. After the ANN is properly trained, it is incorporated into the GA for deep learning to identify the maximum power and corresponding operating conditions, which shows that the fuel cell configuration could achieve a maximum power of about 0.78 W/cm2 at 368.8 K. Additionally, the combined ANN-GA method is employed to identify the maximum powers and their operating conditions under eight typical operation temperatures in the range of 323–373 K. The deep-learning results reflect the major physical and electrochemical processes that govern fuel cell performance and are validated against the 3D multiphysics model. The results demonstrate that the combined ANN-GA method is suitable to predicting fuel cell performance and identifying operation parameters for the maximum powers under various temperatures, which is important to practical system design and rapid control in fuel cell applications.

Polymer electrolyte membrane (PEM) fuel cells produce water as byproduct, which may cause electrode “flooding” and reduce cell performance. In operation, water usually builds up downstream in the gas flow channel due to the water production by the oxygen reduction reaction (ORR), leading to a water spatial distribution. In this study, a convolutional neural network (CNN) is presented to analyze neutron radiography images to obtain water spatial variation under various operating conditions. 5 and 10 segments of a fuel cell are analyzed for spatial variations. Image pre-processing treatments are carried out to improve the convolutional neural network accuracy to 96.6%. The results show that liquid water emerges at a position around 55% downstream for 50% relative humidity while the entire cell is subject to two-phase flow for 100% relative humidity under a co-flow configuration. Large water content is present in most of the segments and the near-outlet segment for the counter-flow and co-flow configurations, respectively. In addition, the quad-serpentine cell exhibits more water accumulation than the single serpentine one in most downstream segments. The convolutional neural network results agree well with the data obtained from a pixelation image processing method with an accuracy of 91.8%. Compared with conventional pixelation methods, the convolutional neural network method performs better in speed for high-resolution images. It also shows that the current CNN tool fails to predict local water for small spatial scales, such as 10 segments, which leads to a large error (>27%) in prediction.